[whatmodel.sh](../../README.md) / WizardLM 13B V1.2
# WizardLM 13B V1.2
Microsoft · released 2023-07 · 13B params · llama2
**score 38.1** ±1.1 · #594 of 732 · from 14 benchmark results across 5 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks WizardLM 13B V1.2 among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃●▇█▇▅▄▃▁ #374 of 469 5.7% 4
chat & writing ▁▁▁▃●▄▆█▆▇▇▃ #350 of 402 51% 4
math ▁▁●▅█▆▄█▄▂▁▁ #390 of 431* 10% 1
coding · #374 of 469 · 5.7% of its score
Beats [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [Qwen3-VL 2B Thinking](qwen3-vl-2b-thinking.md), [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md) here, though they rank higher overall. Trails [Qwen2.5 1.5B Instruct](qwen2-5-1-5b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
CodeReviewQA: solution identifica… 4.6% ▃··●▂▂▃▂█▃▄▂ beats 5/47 medium · avg 51% 2.2% sharp · noisy
CodeReviewQA: automated code refi… 22.9% ▁▁▁▁▁▁▂▂●█▅▄ beats 15/47 hard · avg 32% 2.0% typical
CodeReviewQA: change localisation… 0.1% ●▂▁▁▂▂▂▃▅▅▅▇ beats 0/47 hard · avg 33% 1.4% sharp · too hard for it · noisy
CodeReviewQA: change type recogni… 65.1% ▁▁▁······▁●█ beats 12/47 easy · avg 67% 0.1% blunt · at its level · noisy
chat & writing · #350 of 402 · 51% of its score
Beats [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md), [QwQ-32B-Preview](qwq-32b-preview.md) here, though they rank higher overall. Trails [Gemma 3 270M IT](gemma-3-270m.md), [SmolLM2 360M Instruct](smollm2-360m.md), [Granite 4.0 H 350M](granite-4-0-h-350m.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled 14.5% ▁▂▂▃●▆█▆▄▄▁▁ beats 35/122 medium · avg 35% 24% sharp
LMArena (text) 1149.6 ▁▁▁▂●▃▃▆▆▇█▃ beats 39/385 avg 1260 13% blunt
Polyrating rating (bias-aware re-… 1083.0 ▂▁▂▁▃▇●▆▇▄▂▂ beats 39/95 avg 1187 12% typical
AlpacaEval 1.0 win rate (vs text-… 89.2% ▂▂·▆·▇▃▆●▆▄▂ beats 29/45 very easy · avg 94% 2.6% sharp · noisy
math · #390 of 431 · 10% of its score
Beats [Yi 34B Chat](yi-34b-chat.md), [Llama 3 8B Instruct](llama-3-8b.md), [Command R (03-2024)](command-r-03-2024.md) here, though they rank higher overall. Trails [GPT-Neo 125M](gpt-neo-125m.md), [GPT-2 XL (1.5B)](gpt-2-xl.md), [GPT-Neo 1.3B](gpt-neo-1-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K 48.6% ▂▂▂▂▃▄▅●██▆▄ beats 83/206 easy · avg 80% 10% sharp · at its level
other tests (5)
test result vs the others in its run difficulty counts why
BIG-Bench Hard 38.5% ▁▁▁▁▂●▅▆█▃▃▁ beats 33/155 medium · avg 63% 11% sharp
ARC Challenge 50.4% ▄▁▁▂▁▄█●▃▂▃▂ beats 88/140 easy · avg 82% 7.6% sharp · at its level
MMLU 42.8% ▁▁▁▁▁▂●▅██▆▄ beats 34/214 medium · avg 72% 6.9% at its level
AGIEval English 38.3% ●▄▄··▄··▄█·█ beats 0/9 easy · avg 65% 5.4% at its level
ARC Easy 69.0% ▁▃▂▄▆●▅▃▁··▁ beats 60/100 easy · avg 85% 1.9% blunt · at its level
source results share of the score
Orca 2: Teaching Small Language Models … 6 43% ▇▇▇▇▇
AlpacaEval 1.0 and 2.0 leaderboards 2 26% ▇▇▇
LMArena leaderboard dataset 1 13% ▇▇
Polyrating: A Cost-Effective and Bias-A… 1 12% ▇
CodeReviewQA: The Code Review Comprehen… 4 6% ▇
prior: pull toward the middle 1% ▇
weighted average = 38.1
Licences: Orca 2: Teaching Small Language Models How to Reason ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run WizardLM 13B V1.2 (Q4_K_M, needs ~7.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGUF/tree/679e00967f7b77afa912f4bd5f75da7b7a4ebbee))
```sh
llama cli -hf TheBloke/WizardLM-13B-V1.2-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf TheBloke/WizardLM-13B-V1.2-GGUF:Q4_K_M
```
Other versions: Q8_0 (~13.8 GB) `llama cli -hf TheBloke/WizardLM-13B-V1.2-GGUF:Q8_0`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run WizardLM 13B V1.2 (Q4_K_M, needs ~7.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGUF/tree/679e00967f7b77afa912f4bd5f75da7b7a4ebbee))
```sh
lms get https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~13.8 GB) `lms get https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGUF@Q8_0 -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run WizardLM 13B V1.2 (Q4_K_M, needs ~7.9 GB, from [huggingface.co/TheBloke](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGUF/tree/679e00967f7b77afa912f4bd5f75da7b7a4ebbee))
```sh
ollama run hf.co/TheBloke/WizardLM-13B-V1.2-GGUF:Q4_K_M
```
Other versions: Q8_0 (~13.8 GB) `ollama run hf.co/TheBloke/WizardLM-13B-V1.2-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (fp16, needs ~26.03 GB, from [huggingface.co/WizardLMTeam](https://huggingface.co/WizardLMTeam/WizardLM-13B-V1.2/tree/cf5f40382559f19e13874e45b39575171ca46ef8))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='WizardLMTeam/WizardLM-13B-V1.2', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/WizardLMTeam](https://huggingface.co/WizardLMTeam/WizardLM-13B-V1.2)
[interactive version](https://whatmodel.sh/model/wizardlm-13b-v1-2) · [JSON](https://whatmodel.sh/api?format=json&all=1)